Spatial approaches to ecological population monitoring and management

Tuesday, June 15 at 07:45pm (PDT)
Wednesday, June 16 at 03:45am (BST)
Wednesday, June 16 11:45am (KST)

SMB2021 SMB2021 Follow Tuesday (Wednesday) during the "MS10" time block.
Note: this minisymposia has multiple sessions. The second session is MS09-ECOP (click here).

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Tae-Soo Chon (Pusan National University/Ecology and Future Research Association, Republic of Korea), Fugo Takasu (Nara Women’s University, Japan)


Population stability is at stake in ecosystems due to abrupt establishment or unexpected extinction of species, resulting from anthropogenic disturbances (e.g., industrial development, climate change) directly and indirectly. In ecology population dispersal phases of entering, establishment and proliferation/extinction are presented on spatial domain through individual life processes including reproduction, death, movement and environmental adaptation. Consequently, the degree of internal and external constraints would be enormous during the dispersal processes: objective methods are required for expressing population dispersal effectively. In the symposium we focus on how methodologies could be applied to addressing complex spatial phases for monitoring and management either locally or globally. In small scale stochastic processes on spatial habitat change will be dealt with, utilizing models on behavior state determination (e.g., Markov chain, Hidden Markov model, machine learning). In large scale the causality of population increase/decrease will be addressed in spatio-temporal domain using spatially explicit models, individual based models, network models and other data-driven models to obtain information on monitoring, risk factor analysis, prognosis and management in population dispersal. The target populations for model applications include terrestrial/aquatic species and disease. The participants could join the session and share information on model developments and practical applications.

Hyo Gyeom Kim

(Chonnam National Univ, Republic of Korea)
"Importance of spatial clustering and environmental parameters on classification of the relationships between chemical composition of water body and sediment, and indices of various trophic level biotic communities in river system"
Water and sediment quality influence the biotic communities, and the relationships between the chemical compositions and the communities concurrently give an important information for the regulation and management of river system. Our hypotheses are that the importance of spatial clustering and environmental parameters vary among trophic levels. To address these issues, we applied the clustering results from self-organizing map (SOM) and geo-self-organizing map (Geo-SOM) as a random effect for linear mixed-effect models (LMMs). The datasets were composed of 12 water-quality and 10 sediment variables with indices of benthic diatoms, macroinvertebrates, and fish communities surveyed from 84 stations of rivers of South Korea for 2 years. The SOM proposed 8 clusters based on the relationships between parameters, and the Geo-SOM proposed 13 clusters based on those relationships plus the geographical characteristics. Inclusion of the random effects to SOM and Geo-SOM clustering improved the performance of all the LMMs. In particular, benthic diatom index was best explained with the Geo-SOM clusters, while macroinvertebrate and fish indices were best explained with the SOM clusters. This indicates that benthic diatoms appear to be more affected by spatial heterogeneity caused from the effects of either local pollutant variables or land-use patterns. While the SOM and Geo-SOM suggested that water quality variables were more important than sediment variables, the LMM revealed the importance of Cu for diatom, Cr for macroinvertebrate, and As for fish communities. The parameter for geographical tolerance is useful for determining the necessity of spatial clustering for each trophic level, and the combined method of LMM and SOM provides us an efficient means of establishing target environmental parameters.

Byungjoon Min

(Chungbuk National University, Republic of Korea)
"Identifying an influential spreader in epidemics on meta-population models"
Identifying the most influential spreaders is one of the most important problems in epidemic modeling. So far, some approaches have attempted to rank the influence of spreaders in meta-population models based on heuristics. Here, we derive a theory for calculating the expected size of epidemic outbreaks originated from a single node in a network with a meta-population model by using a message-passing approach. We also test and validate our theory using real-world airline data. Our study provides an analytical tool to predict the most dangerous city for epidemic spreading.

KyoungEun Lee

(National Institute of Ecology, Republic of Korea)
"Network analysis for predicting invasive alien species dispersal in a novel cell-based metapopulation model"
We introduce a cell-based metapopulation model for muskrat (Ondatra zibethicus) dispersal dynamics in the Geum River watershed area in Korea. Muskrat dynamics on the cell is described by a phenomenological metapopulation rate equation, including growth rate, carrying capacity, Allee effect, and especially moving tendency as a function of muskrat population and habitat preference. Diffusive spreading of muskrats is proportional to the growing rate and diffusive deviation rate, whereas decreasing with the Allee effect. The dynamical network using causal decomposition methods was effective in addressing key network properties such as hubs and edge strength similarity. Numerical database construction along with simulation-based prediction framework will enable us to identify ecological as well as environmental properties in revealing invasion causality of muskrats in the target area (Geum River, in this study). Network approaches to modelling and analysing the spreading dynamics can be useful for detecting significant habitats and eco-corridors for either survival or management of invasive species. Furthermore, the dynamical network analyses can be applied to the control scenarios of invasive species concurrently with system sensitivity tests.

Fugo Takasu

(Nara Women’s Univ, Japan)
"Estimation of spatial interaction kernel from time series data - a point pattern approach"
In spatial and invasion ecology, more and more empirical data are available and provided as mapped point pattern; individuals' location in space, status, etc., are recorded as time series data. Examples include spatial expansion of tree diseases such as the pine wilt disease and epidemic/meta-population dynamics, a process made of infection/colonization and recovery/local extinction, etc., over space. Observed time series data as point pattern, however, often contain sampling errors, noises and factors driven by unknown processes, all of which make it difficult to explore true mechanistic processes involved behind the data. In order to explore mechanistic interactions at individual level, we aim to explore methods that better estimate spatial 'interaction kernel'. As an example, in this talk, we extend the classical epidemic models to stochastic point pattern models where infection rate depends on the distance from infectious to susceptible with a certain functional form (infection kernel). We then explore several methods that better estimate the infection kernel based on the time series data generated from the stochastic model. Results of our analyses will be presented and discussed. Our approach is a kind of 'inverse problem' in which we explore methods that better estimate unknowns from data generated from known processes.

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Virtual conference of the Society for Mathematical Biology, 2021.